Bibliographic record
Abstract
Place names, also known as toponyms, are a fundamental part of our cultural and geographical environment. Like many Indigenous groups, Inuvialuit in what is now northwestern Canada use place names to describe the landscape, guide and warn travellers, and convey important cultural information (Hart 2011, 9). Many efforts are underway to document, restore and promote the use of Indigenous toponyms in Canada, including their submission to provincial and territorial naming authorities (Inuit Heritage Trust 2016). A related means of raising the profile of Inuvialuit place names is their inclusion on maps that are readily accessible to the public. In their ten calls to action for natural science researchers working in Canada, Wong et al. (2020) underscore the need for Indigenous place names to be incorporated, with permission, in maps and text associated with scientific research to recognize the stories and Indigenous Knowledge behind the names (777). This paper is a step in addressing this call to action by presenting the results of an analysis of Inuvialuktun-language place names in the Tuktoyaktuk area. The analysis examines how readily the names are identified in official, and popular non-official sources and discusses implications for promoting Indigenous Knowledge more broadly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".